{"id":"W2927608644","doi":"10.1109/ted.2019.2906943","title":"Scalable Modeling of Transient Self-Heating of GaN High-Electron-Mobility Transistors Based on Experimental Measurements","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Electron Devices","topic":"GaN-based semiconductor devices and materials","field":"Physics and Astronomy","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Centre National de la Recherche Scientifique; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"High-electron-mobility transistor; Transient (computer programming); Materials science; Transistor; Optoelectronics; Thermal resistance; Thermal; Scalability; Electrical impedance; Electronic engineering; Electrical engineering; Computer science; Engineering; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002509572,0.0008024097,0.000418094,0.0002171126,0.000187738,0.0003606227,0.0009520684,0.0006299167,0.001432986],"category_scores_gemma":[0.0005812473,0.0003049253,0.0006729926,0.0002178805,0.0003224715,0.001012755,0.000266145,0.0006108042,0.0004907177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007747093,"about_ca_system_score_gemma":0.0004422712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002020312,"about_ca_topic_score_gemma":0.001567192,"domain_scores_codex":[0.9998332,0.00001669388,0.000006293027,0.0000451452,0.0000776261,0.00002097374],"domain_scores_gemma":[0.9998534,0.00005212003,0.00001946948,0.00004150729,0.00002670921,0.000006817635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008846467,0.0001074723,0.001405667,0.0002443965,0.00004772539,0.0002945885,0.0001536753,0.6880575,0.2869588,0.006533918,0.000777993,0.01532967],"study_design_scores_gemma":[0.000003165603,0.00002780997,0.000443506,0.000005094392,0.000004532299,0.00002797991,0.000007624547,0.976418,0.02190239,0.0005972495,0.0005562903,0.000006391616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2333063,0.000667158,0.7455748,0.0002166234,0.0000761753,0.0002582045,0.001203732,0.002965454,0.01573153],"genre_scores_gemma":[0.9731624,0.0003688369,0.02392911,0.00002677986,0.00001586137,0.0001887546,0.0002691565,0.0001046996,0.001934385],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002020312,"threshold_uncertainty_score":0.005620956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01707664298044693,"score_gpt":0.2506937163702156,"score_spread":0.2336170733897687,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}